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Proceedings Paper

Model-based 3D object recognition by a hybrid hypothesis generation and verification approach
Author(s): Philippe Gingins; Heinz Huegli
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Paper Abstract

We present a model-based 3D object recognition architecture that combines pose estimation derived from range images and hypothesis verification derived from intensity images. The architecture takes advantage of the geometrical nature of range images for generating a number of hypothetical poses of objects. Pose and object models are then used to reconstruct a synthetic view of the scene to be compared to the real intensity image for verification. According to the architecture a system has been implemented and successful experiments have been performed with boxes of different shapes and textures. Recognition with our approach is precise and robust. In particular verification can detect false poses resulting from wrong groupings. In addition, the system provides the interesting features to recognize the true pose of shape-symmetrical objects and also to recognize objects that are ambiguous from their sole shape.

Paper Details

Date Published: 10 October 1994
PDF: 11 pages
Proc. SPIE 2353, Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision, (10 October 1994); doi: 10.1117/12.188936
Show Author Affiliations
Philippe Gingins, Univ. de Neuchatel (Switzerland)
Heinz Huegli, Univ. de Neuchatel (Switzerland)

Published in SPIE Proceedings Vol. 2353:
Intelligent Robots and Computer Vision XIII: Algorithms and Computer Vision
David P. Casasent, Editor(s)

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